Classifying a work machine operation

ABSTRACT

A method for analyzing the use of a work machine is disclosed. In one embodiment, the method may include providing a computer with a neural network on the work machine. Further, the method may include inputting data to the computer, at least a portion of the data associated with a load experienced by one of the components of the work machine. The neural network, when executed by the computer may then classify a current operation of the work machine into one of a plurality of types of operations.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to and the benefit of the filing dateof U.S. Provisional Patent Application No. 60/675,493, filed Apr. 28,2005, which is herein incorporated by reference in its entirety.

Further, this application is related to U.S. patent application Ser. No.11/227,157, filed Sep. 16, 2005 entitled SYSTEMS AND METHODS FORDETERMINING FATIGUE LIFE and U.S. patent application Ser. No.11/227,269, filed Sep. 16, 2005 entitled SYSTEMS AND METHODS FORMAINTAINING LOAD HISTORIES, both of which are herein incorporated byreference in their entirety.

This invention was made with U.S. Government support under cooperativeagreement no. 70NANB2H3064 awarded by the National Institute ofStandards and Technology (NIST).

The U.S. Government has certain rights in the invention.

TECHNICAL FIELD

This disclosure relates generally to a system and method for monitoring,determining, and evaluating the loads, health, and use of a workmachine.

BACKGROUND

A typical work machine, such as, for example, a tractor, dozer, loader,earth mover, or other such piece of equipment, may have any number ofmechanical components and systems that are subject to fatigue damagewhich could lead to structural failures. One method for monitoringfatigue damage on a work machine structure is to perform a manual,visual inspection. However, such a method may be impractical for severalreasons. First, such an inspection may not be as comprehensive asdesired. This may be due, in part, to the difficulty in accessing somecomponents of the work machine, such as when the structure in questionis concealed and cannot be viewed without dismantling a portion of thework machine. Second, a manual inspection of structural systems can onlybe performed on a periodic basis, yet damage and resulting catastrophicfailure still can occur between inspections. Third, a manual inspectionmay not be able to detect how much fatigue damage may have alreadyoccurred in the work machine, or predict the mean time till failure ofone or more machine components based on the fatigue damage. While manualinspection may provide some insight into damage that is visible to aninspector, (e.g., large visible cracks in a machine component), internaldamage may not be readily apparent through manual inspection (e.g.,small internal cracks in a component).

Some systems have been proposed utilizing various ways of monitoringstructures electronically to detect fatigue damage. However, theseproposed systems have not adequately addressed the monitoring ofstructures with rapidly changing load pictures, such as movable workmachines. This is due in part to the way these proposed systems collectdata about the structure. These proposed systems may collect data aboutthe structure at a relatively low sampling rate to ease the computingburden of performing analysis on the data and storing the analysisresults. However, a low sampling rate may entirely miss some load stateswhich endure very briefly.

Many critical load states experienced by a work machine may only endurevery briefly. For example, when a wheel loader is digging and the buckethits a rock, the load state may peak for a few brief moments before therock is broken or dug out. In structures with rapidly changing loadstates, the sampling rate must be high in order to capture these peakload states which may endure only very briefly. If the sampling rate istoo slow to “see” all or most of these critical load states, theanalysis results will not accurately reflect the true condition of thestructure.

However, for a complex structure rapid sampling rates may present anenormous challenge as the computing power required to analyze therapidly sampled data in the traditional manner could be unachievable.

Another proposed system for monitoring the structural integrity of astructure is disclosed in U.S. Pat. No. 5,774,376 to Manning. The '376patent discloses a system for monitoring the structural integrity of amechanical structure utilizing a neural network to analyze data andcharacterize the structure's health. In use, a sensor attached to themechanical structure senses vibrations and generates an output signalbased on the vibrations. The sensor output signal is sent throughcontrol electronics to a neural network that generates an output thatcharacterizes the structural integrity of the mechanical structure.However, the system disclosed in the '376 patent is subject to a numberof shortcomings. Experimental results in the literature have suggestedthat changes in vibration signals that result from the presence ofcracks are small unless the crack has already grown to a considerablesize. The use of vibrations as an input also suggests that the structuremust be excited with frequency content that at least partially activatesone of the natural modes of the structure. Many structures never receivesuch input during normal operation, which would require that theexcitation be delivered in some artificial manner, which could becumbersome or impossible. Furthermore, the '376 patent provides a meansof damage detection only. It does not provide any information on theusage habits or loading that would have been the underlying cause ofthat damage.

SUMMARY OF THE INVENTION

A method for analyzing the use of a work machine is disclosed. In oneembodiment, the method may include providing a computer with a neuralnetwork on the work machine. Further, the method may include inputtingdata to the computer, at least a portion of the data associated with aload experienced by one of the components of the work machine. Theneural network, when executed by the computer may then classify acurrent operation of the work machine into one of a plurality of typesof operations.

A system is also disclosed for classifying an operation performed by awork machine. In one embodiment, the system may include sensors disposedabout the work machine. Each of the sensors being configured to detectone or more parameters associated with the work machine. Further, thesystem may include a first memory storing classification data associatedwith different types of operations performable by the work machine.Also, the system may include a processor configured to receive a signalindicative of at least one of the detected parameters and, based uponthe received signal and the stored classification data, classify anoperation of the machine into at least one of the different types ofoperations performable by the machine.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic illustration of an exemplary work machineconsistent with certain disclosed embodiments.

FIG. 2 is a block diagram of an exemplary monitoring system consistentwith certain disclosed embodiments.

FIG. 3 is a block diagram of an exemplary wireless node consistent withcertain disclosed embodiments.

FIG. 4 is a summary flow chart of exemplary processes performed bymethods and systems consistent with certain disclosed embodiments.

FIG. 5 is a flow chart of an exemplary process for determining unknownloads, consistent with certain disclosed embodiments.

FIG. 6 is a diagrammatic illustration of an exemplary lift armconsistent with certain disclosed embodiments.

FIG. 7 is a flowchart of an exemplary strain calculation processconsistent with certain disclosed embodiments.

FIG. 8 is a flowchart of an exemplary neural network configurationprocess consistent with certain disclosed embodiments.

FIG. 9 is a flow chart of an exemplary operation classification processconsistent with certain disclosed embodiments.

FIG. 10 is a flow chart of an exemplary payload determination processconsistent with certain disclosed embodiments.

FIG. 11 is a diagrammatic illustration of an exemplary body under loadconsistent with certain disclosed embodiments.

FIG. 12 is a flow chart of an exemplary load history building processconsistent with certain disclosed embodiments.

FIG. 13 is a block diagram of an exemplary damage rate histogramconsistent with certain disclosed embodiments.

FIG. 14 is a flow chart of an exemplary data analysis process consistentwith certain disclosed embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments andillustrations. Wherever possible, the same reference numbers will beused throughout the drawings to refer to the same or like parts. Whilespecific configurations and arrangements are discussed, it should beunderstood that this is done for illustrative purposes only.

Methods and systems consistent with the disclosed embodiments performprocesses that determine, among other things, loads, health, and use ofa work machine or components of a work machine. In one embodiment, awork machine may be outfitted with a number of sensors. Some of thesensors may measure information reflecting the orientation and movementof the work machine, such as inclination relative to the ground, and thepositions of the movable parts of the machine. Other sensors may measureinformation about forces acting on the work machine. Additional sensorsmay also measure the strain experienced by certain components of themachine.

Certain forces acting on the work machine, however, may be difficult todirectly measure using these sensors, such as ground engaging forcesacting on the machine's wheels. The disclosed embodiments overcome thisproblem by using the measured forces and strains, along with otherinformation, such as orientation information, to determine unmeasuredforces. The unmeasured forces may be calculated using, for example,traditional Newtonian force balance and stress-strain calculations.Moreover, in certain embodiments, a neural network may be used to morequickly solve for these unknown forces.

Accordingly, the type of data used and determined in certain disclosedembodiments may include different types of data. One type may bemeasured data associated with raw measured information reflecting forcesexperienced by a component or structure (e.g., pressure in a cylinder,etc.). A second type of data may be measured strain data associated withactual strains experienced by a component or structure. For example,sensors on opposite ends of a component may provide measured informationthat reflect the strain experienced by that component. Collectively, thefirst and second types of data may define the constraints of the stateof a given body (e.g., a component, set of components, the entire workmachine, etc.). Another type of data may include the unknown load datacalculated using the measured data.

Once the measured forces are measured, and the unmeasured forces arecalculated, methods and systems consistent with certain embodiments maygenerate a complete free body diagram of a portion (e.g., one or morecomponents) or the entire work machine. Based on the complete free bodydiagram and the orientation data associated with the work machine, or acomponent thereof, the strain at any desired point on the work machinemay be calculated. In certain embodiments where technology is used toobtain a fast sampling rate of the free body diagram, the calculatedstrain at a given point on the work machine may be used to continuouslyupdate a prediction of the remaining fatigue life of a structuresurrounding the given point.

In certain embodiments, the data used in determining the remainingfatigue life of a work machine may be used for other purposes. Forexample, the data associated with the complete free body diagram, andother data reflecting the orientation and movement of the work machine,may be used to classify an operation of the work machine at any givenpoint in time into one of several discrete operating states. Forexample, the data may be used to determine whether the work machine isdigging or roading at any given point in time. As another example, thedata of the free body diagram may be used to compute the weight ofmaterial in a bucket of the work machine following a digging operation.As another example, the data of the free body diagram and theorientation data, along with information reflecting the current positionof the work machine's center of gravity at a given point in time oroperation may be used to determined whether the work machine is indanger of tipping. In another example, the data of the free body diagrammay be used to determine historically high loading states of the workmachine, or individual components thereof, that are experienced duringactual operation. The historical high loading state data may be used tobetter understand the forces experienced by the work machine, orcomponents thereof, to assist in the design or manufacture stagesassociated with the machine. It should be noted that the above examplesare not intended to be limiting, as there are many uses for using thedata collected and calculated by the methods and systems disclosedherein.

Exemplary Work Machine

FIG. 1 shows an exemplary work machine 100 that may incorporate anelectronic health monitoring system as disclosed herein. Work machine,as the term is used herein, refers to a fixed or mobile machine thatperforms some type of operation associated with a particular industry,such as mining, construction, farming, etc. and operates between orwithin work environments (e.g., construction site, mine site, powerplants, etc.). A non-limiting example of a fixed machine includes anengine system operating in a plant or off-shore environment (e.g.,off-shore drilling platform). Non-limiting examples of mobile machinesinclude commercial machines, such as trucks, cranes, earth movingvehicles, mining vehicles, backhoes, material handling equipment,farming equipment, marine vessels, aircraft, and any type of movablemachine that operates in a work environment. As shown in FIG. 1, workmachines 100 is an earth moving type work machine. The type of workmachine illustrated in FIG. 1 is exemplary and not intended to belimiting. It is contemplated that the disclosed embodiments mayimplement any type of work machine.

The exemplary work machine 100 may include a rear end 102 and a frontend 104. The rear end may include an engine housing 106 and an operatorstation 110. The front end 104 may include one or more lift arms 112,one or more tilt levers 114, one or more tilt links 116, a workimplement 118, and a non-engine end frame 120. In the example of workmachine 100 being a wheel loader, work implement 118 is powered andcontrolled by a number of actuators, including a tilt actuator 122 and alift actuator (not shown).

Work machine 100 may include front and rear ground engaging devices,such as front wheels 124 and rear wheels 126 that support work machine100. The engine housing 106 may include a power source, such as anengine 108, that may provide power to the front and/or rear wheels 124,126.

To control work machine 100, including work implement 118, an operatormay manipulate one or more input devices that may be housed within theoperator station 110. The input devices may ultimately control workmachine 100 by extending and retracting hydraulic steering actuators,the tilt actuator 122, the lift actuator, and controlling engine 108.Although the health monitoring system is discussed with reference to awheel loader, the principles and systems described herein are equallyapplicable to any work machine that may be used to perform a task.

Exemplary Monitoring System

FIG. 2 shows an exemplary monitoring system 200 consistent with certaindisclosed embodiments. In one embodiment, monitoring system 200 may beimplemented on a work machine that has moving parts, a rapidly changingload state, etc., such as work machine 100. Further, monitoring system200 may be configured to perform health and usage monitoring functionsassociated with the operations of work machine 100. That is, monitoringsystem 200 may be configured to process information affiliated with thedynamic load changes experienced by machine 100. Further, monitoringsystem 200 may be configured with hardware and/or software that enablesit to process work machine-related data in real time, as well asgenerate, store, and manage information related to raw data obtainedfrom one or more machine components, such as sensors. In this regard,the monitoring system may maintain a manageable set of information foranalysis and reporting. Moreover, monitoring system 200 may includewireless communication elements that enable moving and non-movingcomponents of work machine 100 to communicate without wired data links.Other aspects may be implemented by the disclosed embodiments and theconfiguration of monitoring system 200 is not limited to the exampleslisted above or described below.

Sensor Network

In the exemplary embodiment shown, the system 200 includes a wiredsensor network 202, a wireless sensor network 204, a central computer206 (which may be a digital signal processor (DSP)), and a memorycomponent, such as a vehicle database 208. Wired sensor network 202 andwireless sensor network 204, together may include sensors for detecting,for example, hydraulic pressures in actuators, positions of cylinderrods, implement linkage angles, velocities and accelerations, steeringarticulation angle, strain on bolts forming structural joints, vehicleground speed, inclination relative to the Earth, and forces oninstrumented pins in linkages and other structures. Data obtained bywired sensor network 202 and wireless sensor network 204 may be used toperform structural health and usage monitoring.

The sensor networks 202 and 204 may each be configured to collect dataindicative of loads acting on work machine 100. Although FIG. 2 shows awired sensor network 202 and wireless sensor network 204, either networkmay be implemented as a wireless or wired network. In one example, wiredsensor network 202 may include an orientation sensor 210, one or morehydraulic pressure sensors 212, one or more cylinder position sensors214, one or more work implement position sensors 216, one or more workimplement velocity sensors 218, load pins 220, and bending bridges 222.Generally, these may all be referred to as “sensors.” In addition, wiredsensor network 202 may include interface electronics 224 and/or anelectronic control module (ECM) 226. In other embodiments, wired sensornetwork 202 of the exemplary health and usage monitoring system 200 mayinclude additional sensors and/or different sensors or other components.

In general, the sensors implemented by work machine 100 (e.g., sensors210-228) may be separated into three categories: sensors that senseorientation and movement of the machine, sensors that measure loads(e.g., cylinder pressure sensors, strain gauges on the rod ends ofhydraulic cylinders, etc.), and sensors that sense strain at some point,such as a sensor on a structural frame within work machine 100. Thenumber and position of the sensors implemented within work machine 100may depend on the type of work machine, the type of component(s) withinwork machine, the desired and actual use of the machine, and otherfactors. For example, a certain number of sensors associated with thefirst two categories may be selectively positioned in order to provideadequate information to constrain the problem of generating the entirefree body diagram of the machine or machine component. The sensors fromthe third group, however, may be positioned in locations to provide abase set of measured data to compare to calculated strains (e.g., normalstrain values). Further, based on the location of certain machinecomponents, or other sensors, a sensor positioned on these certainmachine components may be wired or wireless.

Orientation sensor 210 may be one or more inclinometers disposed on workmachine 100 to measure one or both of pitch and roll of work machine 100relative to the Earth. Hydraulic pressure sensors 212 may be associatedwith a hydraulic system to detect fluid pressure. In one exemplaryembodiment, pressure sensors 212 may be associated with a cylinder headof a hydraulic actuator, such as the tilt actuator 122. Hydraulicpressure sensors 212 may be disposed at other locations about workmachine 100 to measure hydraulic pressures. Pressure sensors 212 mayprovide information regarding one or more forces acting on the structureof work machine 100 at connection points of the hydraulic actuator.

Cylinder position sensors 214 may be configured to sense the movementand relative position of one or more components of work machine 100,such as components of front end 104. Position sensors 214 may beoperatively coupled, for example, to actuators, such as tilt actuator122. Alternatively, position sensors 214 may be operatively coupled tothe joints connecting the various components of front end 104. Someexamples of suitable position sensors 214 include, among others, lengthpotentiometers, radio frequency resonance sensors, rotarypotentiometers, machine articulation angle sensors and the like.

Work implement position sensors 216 may be associated with workimplement 118 in a manner to detect its position. In one exemplaryembodiment, work implement position sensors 216 are rotary positionsensors disposed at pin connections on work implement 118. Otherposition sensors also may be used including, among others, radiofrequency resonance sensors, rotary potentiometers, angle positionsensors, and the like. Work implement acceleration sensors 218 mayinclude an accelerometer or other type of sensor or sensors configuredto monitor acceleration and may be associated with work implement 118 ina manner to properly detect acceleration of any desired point.Velocities may also be obtained based on the time-derivative of positionsensors for the bucket or other similar component of work machine 100.

Load pins 220 may be configured to measure force in x and y-axes ininner and outer shear planes of a pin and may be instrumented with, forexample, one or more strain gauges. The load pins 220 could beinstrumented with strain gauges on the outer or inner surface of thepin, or they could be instrumented with some other technology designedto react to the stress state in the pin, such as magnetostriction. Loadpins 220 may be disposed at joints on work machine 100. In one exemplaryembodiment, load pins 220 are disposed at joints connecting componentsof work implement 118 and/or connecting the actuators, such as tiltactuator 122, to work implement 118. Load pins 220 may be disposed atother joints about work machine 100.

Bending bridges 222 may be configured to measure strain in or alongsurfaces, such as, for example, along sides of lift arm 112. In oneexemplary embodiment, the bending bridges may include, for example, fourstrain gauges. In one exemplary embodiment, the strain gauges on bendingbridges 222 may be configured to provide one combined output.

Interface electronics 224 may be in communication with the sensors, suchas load pins 220 and bending bridges 222, and may be configured toreceive data signals from the sensors, process the data signals, andcommunicate data to computer 206. Interface electronics 224 may include,for example, a module including, for example, a PIC18F 258microprocessor, 32 KB Flash, 1.5 KB RAM, 256 bytes EEPROM, CAN 2.0Binterface with 12 bit external A/D sampling, and 4 strain channels. Inone exemplary embodiment, health and usage monitoring system 200 mayinclude nine interface electronics 224, each associated with load pins220 and bending bridges 222. The interface electronics may be configuredto communicate time-stamped and synchronized information, along withsensed values.

Electronic control module (ECM) 226 may contain a processor and a memorydevice, and may be configured to receive data signals from sensors 210,212, 214, 216, 218, process the data signals, and communicate data tocomputer 206. The processor in ECM 226 may be a microprocessor or otherprocessor, and may be configured to execute computer readable code orcomputer programming to perform functions, as is known in the art. Thememory device in the ECM 226 may be in communication with the processor,and may provide storage of computer programs and executable code,including algorithms and data enabling processing of the data receivedfrom sensors 210, 212, 214, 216, 218. In one exemplary embodiment, ECM226 may include a MPC555 microprocessor, 2 MB ROM, 256 KB RAM, and 32 KBEEPROM.

Wireless sensor network 204 may include a number of wireless nodes 228and a gateway node 230. Wireless nodes 228 may be disposed about workmachine 100 and may be configured to communicate data signalsrepresentative of measured strain to other wireless nodes, andultimately to gateway node 230. One exemplary wireless node 228 is shownin FIG. 3. The wireless node 228 may include one or more strain gauges232, conditioning electronics 234, an RF module 236, and a transceiver238. Strain gauges 232 may be configured to measure local strain on acomponent of work machine 100. In one exemplary embodiment, straingauges 232 may include a rosette providing three sets of strain datafrom multiple strain gauges so that the strain tensor may be completelydefined at each sensed point of a machine 100. Electronics 234 mayprocess or filter a signal representative of the strain from straingauges 232 and may communicate data representative of the strain to RFmodule 236, which may communicate the data to other wireless nodes 228and/or to gateway node 230 using transceiver 238. It should be notedthat wireless node 228 in FIG. 3 is exemplary only, and may beconfigured in any known manner. In one exemplary embodiment, wirelessnode 228 may include a receiver or a transmitter instead of transceiver238. In another exemplary embodiment, wireless nodes 228 may eachinclude a processor and memory for processing signals from strain gauges232. Wireless nodes 228 may include other components, including a powersource, such as a battery. Other configurations would be apparent to oneskilled in the art.

In one exemplary embodiment, measurements from wireless nodes 228 may betagged with timestamps to allow computer 206 to synchronize themeasurements from the different nodes and from the wired sensor network202. In one exemplary embodiment, wireless nodes 228 may concatenatemeasurements over relatively long periods before data transmission.Means for synchronizing the measurements may be incorporated into thetransmitted data.

To minimize power usage, wireless nodes 228 may compress and accumulatetheir data and then send the accumulated data periodically, over aprogrammable time interval. In one exemplary embodiment, wireless nodes228 are programmed to send their accumulated strain data over two secondintervals. This means that computer 206, in addition to scaling thesensor data, may rebuild the time history of the actual strains.

The gateway node 230 may be in communication with wireless nodes 228 andmay be in communication with computer 206. Accordingly, the gateway node230 may be configured to communicate data indicative of the straincollected by wireless nodes 228 to computer 206.

It should be noted that the health and usage monitoring system 200 alsomay be operable with a single wired network or a single wirelessnetwork, rather than simultaneously employing a wired and a wirelessnetwork. Further, the number of gauges and other instruments used tocollect data may vary depending upon the application and type of workmachine 100.

Computer 206 may be in communication with the gateway node 230, the ECM226, and the interface electronics 224. Computer 206 may be configuredto receive data signals, process the data signals, and communicate datato the vehicle database 208. Computer 206 may be one or more processorsconfigured to execute computer readable code that perform processesconsistent with certain disclosed embodiments, such as functions todetermine the life of or load on one or more components of work machine100. In one exemplary embodiment, computer 206 may be associated with adata transfer device (not shown) that may provide output of data fromcomputer 206 and/or vehicle database 208. The data transfer device couldbe a port connectable to a service tool, such as a laptop computer, ahand-held data device, and a wireless transmitter, among others.Computer 206 may include, for example, resources to process varyingnumbers of inputs. For instance, computer 206 may execute program codethat stores data in a first-in-first-out buffer at maximum expectedinput sampling rates. Additionally, computer 206 may be configured toperform algorithms consistent with the health and usage monitoringembodiments disclosed herein, such as processing data through one ormore neural networks, performing floating-point matrix calculations,etc. In one exemplary embodiment, computer 206 may be an MPC5200 typeprocessor using a QNX real-time operating system.

Vehicle database 208 may include one or more memory devices that storedata and computer programs and/or executable code, including algorithmsand data enabling processing of the data received from gateway node 230,ECM 226, and interface electronics 224. The memory devices may be anytype of memory device(s) known in the art that is compatible withcomputer 206. Vehicle database 208 also may be configured to store datacalculated by computer 206 and may be configured to store computerprograms and other information accessible by computer 206.

In one embodiment, database 208 may store neural network software that,when executed by computer 206, performs neural network processesconsistent with the disclosed embodiments. A neural network is designedto mimic the operations of the human brain by determining theinteraction between input and response variables based on a network ofprocessing cells. The cells, commonly known as neurons or nodes, aregenerally arranged in layers, with each cell receiving inputs from apreceding layer and providing an output to a subsequent layer. Theinterconnections or links that transfer the inputs and outputs in aneural network are associated with a weight value that may be adjustedto allow the network to produce a predicted output value. Neuralnetworks may provide predicted response values based on historical dataassociated with modeled data provided as independent input variables tothe network. Neural networks may be trained by adjusting the data valuesassociated with the weights of the network each time the historical datais provided as an input to allow the network to accurately predict theoutput variables. To do so, the predicted outputs are compared to actualresponse data of the system and weights are adjusted accordingly until atarget response value is obtained.

Overview Process

As explained, methods and systems consistent with the disclosedembodiments collect, determine, and analyze data associated with forcesexperienced by work machine 100. Based on the data, embodiments maycalculate unknown variables, such as unknown loads, classify workmachine operations, generate free body diagrams, calculate strains,determine the life of a component(s) of work machine 100, and evaluateand predict the life of component(s) of work machine 100 or of machine100 itself. FIG. 4 shows a flowchart summarizing some exemplaryprocesses that may be performed by methods and systems consistent withcertain disclosed embodiments.

Initially, in one embodiment, work machine 100 may experience a startupstage that may include powering up work machine 100 or otherwiseactivating monitoring system 200. At startup, computer 206 may send oneor more signals to “wake up” wireless nodes 228. Sending the signal mayinclude activating or commanding an intermediary component to route thewake up signal to other connected components. For example, computer 206may issue an activation signal to gateway node 230 or a first wirelessnode 228, which in turn notifies one or more remaining wireless nodes228 to awake and begin processing. At the same time, or at a differenttime, computer 206 may communicate a signal to one or more wired sensors210-222 in a similar manner as described above, to begin collecting andprocessing inputs.

Once awakened, one or more of sensors 210, 212, 214, 216, 218, 220, 222,228 (hereinafter collectively referred to as “sensors 210-228”) maycollect raw measured data from their associated components (Step 405).For example, sensors 210-228 may be disposed in different locations onfront end 104 of work machine 100 to measure one or more parameters oflift arm 112, tilt lever 114, tilt link 116, work implement 118, and/ornon-engine end frame 120. Once obtained, the raw measured data may betime-stamped and communicated to computer 206 for subsequent processing.

In one embodiment, computer 206 may determine whether the data receivedfrom each sensor is reliable. In one exemplary embodiment, computer 206may determine data reliability executing neural network softwareconfigured to recognize reliable and unreliable data for each of thetypes of data received from sensors 210-228. For example, the neuralnetwork executed by computer 206 may receive as input data from a sensorshowing a flatline channel. Based on predetermined configurations, theneural network may recognize the flatline channel as an indicator thatthe particular sensor has ceased functioning properly, and therefore,its data may be unreliable. In another example, the neural network mayreceive sensor data that is determined to include a certain threshold ofreliable data (e.g., a majority of data). For example, the neuralnetwork may recognize that a majority of a sensor's data is reliable,but also includes an occasional outlier of incorrect data. The neuralnetwork may recognize this outlier as unreliable data. Therefore, basedon an analysis of the data itself, the neural network may classify thedata as either reliable or unreliable.

In other exemplary embodiments, computer 206 may use other processes todetermine whether the data is reliable. For example, in one exemplaryembodiment, computer 206 may perform a logic-based process that monitorsthe first and second time-derivatives of the incoming data signals. Inthis embodiment, the flatline channel may have a zero first derivativeat all times, and therefore, may be identified as unreliable. Inaddition, an otherwise correctly functioning channel with an occasionalnoise spike may have unusually large higher derivatives at the timesassociated with the noise spike. Therefore, again, the noise spike maybe identified as unreliable.

If computer 206 determines the raw measured data to be unreliable, theunreliable data may be discarded and substituted with a data determinedusing interpolation techniques known in the art (Step 410).Alternatively, when the unreliable data is a strain measurement for aparticular component of machine 100, the unreliable strain measurementmay be substituted with a previously calculated strain for thatcomponent. In some exemplary embodiments, the unreliable data isdiscarded and the strain calculation process continues withoutsubstituting calculated data. In other exemplary embodiments, theprocessing iteration is suspended due to the unreliable data, andcomputer 206 resumes the strain calculation process at step 405. Inother exemplary embodiments, other components of machine 100 may screenthe measured data for reliability, such as gateway node 230, ECM 226,electronics 224, and/or at any of sensors 210-228. In these embodiments,the gateway node 230, the ECM 226, the electronics 224, and/or thesensors 210-228 may be configured with hardware and/or software that iscapable of detecting unreliable measured data.

Also, in step 410, computer 206 may determine that measured strain datais included in the measured data collected in step 405. In certainembodiments, computer 206 may determine measured strain data based oncollected measured data. For example, based on sensor data collectedfrom sensors positioned on opposing sides of a machine component,computer 206 may determine the strain imposed on the component, or aportion thereof. Thus, in step 410, computer 206 may produce measureddata of a first category (i.e., raw measured data reflecting forces on aparticular component, such as pressure, etc.) and measured data of thesecond category (e.g., measured strain data).

In one embodiment, based on the measured data provided in Step 410,computer 206 may determine the load(s) on various structural bodieswithin work machine 100 (e.g., one or more components of work machine100) (Step 415). Computer 206 may use the load data to determine thestrain and fatigue life associated with one or more monitored componentsof work machine 100, as described further below. In calculating theload(s), computer 206 may convert the measured strain into aproportional quantity that reflects information that is more relevant tothe actual physical strain values on the measured component than themeasured strain data provided in step 410. For example, computer 206 mayconvert the measured raw strain data associated with an instrumented pinlocated on a machine component into data representing the resultant loadand local moments for the pin. In another example, computer 206 mayconvert measured axial strain data for a cylinder rod to load-baseddata, which may be then shifted in order to match load data calculatedfrom head-end and rod-end pressure readings associated with the cylinderrod. In this example, a strain gauge may measure strain data for thecylinder. The strain gauge may provide the measured strain data tocomputer 206 for determining the load applied to the cylinder when it is“bottomed-out” (i.e., when a rod within the cylinder is fully extended,thus forcing the piston to the edge of one end of the cylinder). Incertain embodiments, computer 206 may shift received strain data to acorrected value. Because strain gauges may sense only strain relative tothe time when the gauge was activated (i.e., turned on and operational),computer 206 may execute software that performs a linear regressionanalysis, or similar type of analysis, on the strain data to create abest-fit expression that is used to offset the loads calculated from therod strain so that they are in agreement with loads calculated fromcylinder pressures when the cylinder is not at either of the-extremelimits of displacement. The linear regression techniques performed bycomputer 206 may be those techniques known in the art.

Also, in addition to cylinder forces, strain gauges may be implementedwithin work machine 100 that measure the axial force in tilt link 116.In this example, computer 206 may execute software that converts themeasured strain data obtained from the strain gauge for tilt link 116 toload-based data using strain to force conversion methods known in theart. In instances where the strain gauge does not measure absolutestrain data values, computer 206 may perform correction processes thatcorrect the measured load on tilt link 116 to represent an absolutestrain value. Computer 206 may then use linear regression analysis, orsimilar processes, to determine the axial load in tilt link 116.

In certain embodiments, tilt link 116 may experience a dump stop eventduring operation of work machine 100. A dump stop event is a conditionwhen tilt link 116 impacts lift arm 112 during operation. The forcesimposed on these elements during such an event may cause fluctuations indetermining the load associated with tilt link 116. As such, computer206 may execute processes that compensate for the forces occurringduring a dump stop event to accurately determine the load experienced bytilt link 116. One process may be configured to provide an estimate ofthe load of tilt link 116 for non-dump stop event states (e.g., when thetilt lever 114 is not in contact with the lift arm 112). A secondprocess may be configured to calculate tilt link 116 load during timeswhen the tilt lever 114 is in contact with the lift arm 112. Each ofthese processes may be based on load determining algorithms andtechniques known in the art and executed by computer 206.

In certain embodiments, one or more wireless strain gauges may beemployed to measure the load on a given component of work machine 100.For instance, a wireless node 228 may be configured as a wireless straingauge for tilt lever 114 that measures its tilt link load. It should benoted that such determinations may be performed using well-knownkinematic equations. Further, to conserve the energy of wireless node228, each node may be configured with a “sleep” mode. For instance, awireless tilt lever 114 strain gauge, and its accompanying wireless node228, may be placed in a low power mode (i.e., “sleep” mode) whenever thetilt lever 114 is not in contact with the lift arm 112. In anotherexemplary embodiment, computer 206 may estimate the load on a givenmachine component using a neural network configured to provide theoutput load based on known neural network programming softwareprocesses. It should be noted that component loads may be determinedusing other configurations and techniques, and the disclosed embodimentsare not limited to the above described examples.

Computer 206 may also determine unknown loads (e.g., unmeasured loads)acting on or within work machine 100, such as all unknown loads for theentire machine, or certain portions of machine 100, such as a frontsection, lift arms, etc. For example, unknown loads may be associatedwith ground interface loads that cannot be measured directly by asensor. In certain embodiments, computer 206 may determine unknown loadsusing a neural network or by employing traditional deterministicsoftware based upon the equations of motion. Embodiments involving aneural network are described further below. In some applications, it maynot be necessary to employ a neural network to determine the unknownloads. In certain embodiments, if inertial loading and contributionsfrom mechanical vibration are not significant, computer 206 may use theNewtonian equations of static equilibrium to determine the unknownloads.

Once computer 206 determines the unknown loads, it may execute softwarethat coordinates system transforms to associate all of the determinedloads (known and unknown) to coordinate data corresponding to one ormore respective components of work machine 100 (Step 425). This processallows computer 206 to generate a free-body diagram of one or more, orall of the respective components of work machine 100, which is describedin further detail below in connection with FIG. 5.

As explained, the data collected and calculated by monitoring system 200may be used to perform one or more processes consistent with certaindisclosed embodiments. In one embodiment, the determined load data maybe used to calculate strains experienced by one or more components ofwork machine 100. For instance, in one embodiment, computer 206 mayobtain an influence coefficient matrix (A) that is stored in a memorydevice within work machine 100 (Step 430). The influence coefficientmatrix (A) includes data reflecting the strain response at a number ofchosen locations of a particular component under the influence of aparticular unit load. Using the influence coefficient matrix (A), andother information, computer 206 may calculate strain data associatedwith one or more components of work machine 100, or for the entiremachine itself (Step 435.) Details regarding the strain calculationsperformed by computer 206 are further described below in connection withFIG. 7.

In another embodiment, the load(s) determined by computer 206 in Step415 may be used to determine a payload of work machine 100 during itsoperations (Step 440). Details of the payload determination processesperformed by computer 206 are further described below in connection withFIG. 10.

In another embodiment, the measured data collected and/or determined bycomputer 206 in Steps 405 and 410, as well as the strain(s) calculatedin Step 435, may be used to determine the fatigue life of one or morecomponents of work machine 100, or of work machine 100 itself (Step445). This information may also be used to determine damage of the oneor more components of work machine 100, or of machine 100 itself. (Step450).

Also, in another embodiment, the calculations performed by monitoringsystem 200 may produce result data that may be used to update theinformation stored in database 208 (Step 460). For example, informationreflecting the payload determined in Step 440 may be stored in database208 for subsequent processing by computer 206 or an off-board systeminterfaced with work machine 200 via communication network (e.g.,wireline or wireless network). Further, any damage data for a particularcomponent(s) determined in Step 460, may be stored as information indatabase 208 that is also accessible for subsequent analysis andprocessing. Similarly, the calculated strains and fatigue lifeinformation determined in Steps 435 and 445 may be stored in database208. In this regard, embodiments may continuously update informationreflecting the health and use of one or more components of work machine100, or of work machine 100 itself, thus providing up-to date statusinformation reflecting the operation of work machine 100, and itscomponents.

Additionally, the data collected and calculated by monitoring system 200may be used to classify an operation of work machine 100 (Step 465.) Forexample, the measured and determined data produced by Steps 405 and 410,the unknown loads determined in Step 420, the coordinate systemtransforms determined in Step 425, and the calculated strains obtainedin Step 435, may be used by computer 206 to automatically determine acurrent operation of work machine 100. Details regarding classifyingoperations of work machine 100 are described below in connection withFIG. 9.

Calculating Unknown Loads to Obtain Free Body Diagram

As explained, methods and systems consistent with certain embodimentsenable monitoring system 200 to calculate unknown loads associated withone or more components of work machine 100. In one instance, computer206 may use a neural network to determine unknown loads during operationof work machine 100. In certain embodiments, the unknown loads may beused to generate a free-body diagram of a given component of workmachine 100. FIG. 5 illustrates an exemplary unknown load determinationprocess consistent with certain embodiments.

Initially, the neural network used to determine the unknown loads shouldbe trained. To do so, in one embodiment, a testing process may beperformed before monitoring system 200 performs run-time determinationsof unknown loads. The testing process may be performed for each of afleet of work machines, for each type of work machine, etc. that isinstalled with monitoring system 200. For exemplary purposes, workmachine 100 is described as being exposed to the testing process,although it should be noted that a work machine of a similar type ofmachine 100 may be used in lieu of testing work machine 100 to train theneural network.

During testing, work machine 100 is operated for a predetermined time,under one or more operational conditions. During this time, measureddata is collected (Step 510). The measured data may correspond to aspecific set of measured data, and collected via sensors that measureforces, and sensors that measure strains representing forces experiencedby one or more, or all, components of work machine 100. The measureddata may then be used as values in, for example, Newtonian staticequilibrium equations, that generate output values reflecting unknownloads of specified locations of one or more components of work machine100 (Step 520). These output values, along with the specified set ofmeasured data, are fed into a neural network to train the network toprovide predicted unknown loads within a predetermined threshold (e.g.,unknown load values within a certain percentage value of the unknownload values calculated using the Newtonian static equilibrium equations)(Step 530). If the neural network does not produce results within thepredetermined threshold, the weights associated with the network may beadjusted until the network produces unknown load output values thatmeets the predetermined threshold criteria.

In one embodiment, the weight values of the network may be defined basedon information determined during previous training of the neuralnetwork. In one embodiment, the neural network may be trained based oncalculated load values associated with areas of a machine component thatare not monitored by sensors during real time operations. Thesecalculations may be performed using a mechanism analysis softwarepackage, such as Pro-Mechanica Motion, that simulates the operation ofwork machine 100 using test data as input. The unknown loads would becalculated as output, and would be used to construct the neural networktraining set.

Once the neural network is trained, its may be stored in a memory devicethat is accessible by computer 206 for execution during operation ofmonitoring system 200. Subsequently, work machine 100 may performoperations (Step 540). During these operations, computer 206 collectsmeasured data in a manner similar to the processes described above inconnection with Steps 405 and 410 of FIG. 4 (Step 550). The measureddata (e.g., measured force data and strain data reflecting forces ongiven components) are fed into the neural network, which produces outputvalues reflecting estimates of the unknown loads of work machine 100(Step 560).

In one embodiment, once computer 206 determines the unknown loads, itmay also execute software that associates all of the determined loaddata to the coordinate systems corresponding to one or more respectivecomponents of work machine 100. This process allows computer 206 togenerate a free-body diagram of one or more, or all of the respectivecomponents of work machine 100 (Step 570). FIG. 6 shows one example oflift arm 112 with its associated loads (shown as arrows without theirrespective load data values) in a free body form illustration. As shown,lift arm 112 may include twenty-eight externally applied loads. Onlysome of these loads may have been directly measured by one or moresensors 210-228. The remainder of the loads may be calculated based uponthe known loads in the manner described above in connection with Step420. Computer 206 may resolve the load data into the appropriatecoordinate system for any structural component using known algorithms,such as trigonometric calculations, that may vary for each type of workmachine 100 and/or each type of component of work machine 100.Alternatively, computer 206 may execute neural network software that hasbeen trained to estimate loads in the correct coordinate system of aparticular component, such as lift arm 112. It should be noted that FIG.6 shows an illustration of one component of work machine 100 includingcoordinate-based loads. The determined load data, relative to theirrespective coordinate data, is stored as data in a memory location thatmay be used to perform other processes consistent with certain disclosedembodiments.

Calculating Strain and Determining Fatigue Life

As explained, computer 206 may execute software that calculates strainsacting on one or more components of work machine 100. FIG. 7 shows aflowchart of an exemplary strain calculation process consistent withcertain disclosed embodiments. Initially, computer 206 may retrieve andanalyze the free body diagram(s) previously determined by computer 206,and described above in connection with FIGS. 5 and 6 (Step 710).Depending on the strains being determined, computer 206 may retrieve andanalyze one or more free body diagrams. For example, to determine allstrains acting upon work machine 100, computer 206 may retrieve andanalyze the free body diagram data associated with all components ofwork machine 100. Alternatively, if computer 206 is determining thestrain of a particular component, it would retrieve and analyze the freebody diagram associated with that component. In certain embodiments,when determining the strains acting on work machine 100, computer 206may process free body diagrams one at a time, to later analyze thecalculated strains of each respective component.

As noted above in connection with FIG. 4, computer 206 may executesoftware to calculate the strains using an influence coefficient matrix(A). As such, computer 206 may retrieve and populate matrix (A)corresponding to the respective component(s) associated with the strainsbeing calculated (Step 720). Each column of matrix (A) may represent thestrain response at a number of chosen locations of a particular machinecomponent (e.g., lift arm 112 shown in FIG. 6) under the influence of aparticular unit load, with all other loads set to zero.

In one embodiment, the influence coefficient matrix (A) may bedetermined by known unit load analysis of a finite element model of thegiven component, as is known in the art. In another exemplaryembodiment, instead of analysis of a finite element model, computer 206may experimentally determine data for selected rows of the influencecoefficient matrix (A) for the given machine component during selectedtime periods of operation of work machine 100, such as initial operationtime periods ranging from start-up of the machine to a certain timeperiod thereafter (e.g., one or more minutes, hours, etc. later). Duringthese time periods of operation, the given component for the matrixunder construction may be presumed to be structurally sound (e.g.,having no fatigue flaws or cracks). Thus, while the component is deemedstructurally sound, the influence coefficient matrix (A) may bedetermined by measuring strains on the component, and performing a leastsquares fit between the known external loads on the component and themeasured strains. Based upon the results of the least squares fitcalculation, the influence coefficient matrix (A) may be populated withthe correct entries. Thus, experimentally determining rows in theinfluence coefficient matrix (A) may be used for high-stress gradientareas in the given component that may be difficult to model using thefinite element method.

The fundamental relationship between measured strain and applied loadfor a quasi-static body is given by the equation below.

$\underset{({r \times 1})}{s} = {\underset{({r \times n})}{A}\;\underset{({n \times 1})}{f}}$

where r=no. of measured strain channels on a body, and n=no. of externalloads on a body. The influence coefficient matrix (A) may be populatedrow-by-row. In one embodiment, the first row of the above equation maybe written in a time-dependent column vector form, for k time steps intothe future, as shown below.

$\begin{Bmatrix}{s_{1}\left( t_{0} \right)} \\{s_{1}\left( {t_{0} + {\Delta\; t}} \right)} \\{s_{1}\left( {t_{0} + {2\Delta\; t}} \right)} \\\vdots \\{s_{1}\left( {t_{0} + {k\;\Delta\; t}} \right)}\end{Bmatrix} = {\begin{bmatrix}{f^{T}\left( t_{0} \right)} \\{f^{T}\left( {t_{0} + {\Delta\; t}} \right)} \\{f^{T}\left( {t_{0} + {2\Delta\; t}} \right)} \\\vdots \\{f^{T}\left( {t_{0} + {k\;\Delta\; t}} \right)}\end{bmatrix}\begin{Bmatrix}a_{11} \\a_{12} \\a_{13} \\\vdots \\a_{1n}\end{Bmatrix}}$

The least-squares best-fit solution for the unknown elements in thefirst row of the influence coefficient matrix is given by the followingequation.a ₁ ′=[F ^(T) F] ⁻¹ F ^(T) s ₁

The above calculations may be repeated for the remaining rows of theinfluence coefficient matrix (A). In one embodiment, the number ofiterations (i.e., k) that may be necessary for computer 206 to generatean influence coefficient matrix (A) that is satisfactory for all timeperiods of operation may be dependent on the amount of load data to beobtained. For example, the number of iterations k may continue untiltest data for each of the different loads has been obtained.

Once the influence coefficient matrix (A) has been fully populated,computer 206 calculates the strain using, for example, known matrixmultiplication techniques (Step 730). For example, computer 206 maycalculate the strain by multiplying a column vector including datareflecting the external loads experienced by the given component by theinfluence coefficient matrix (A). The results of the matrixmultiplication representing the calculated strain for the givencomponent may be stored in a memory location for subsequent processing.In one embodiment, computer 206 calculates the strain for each of themonitored components of work machine 100 to provide a representation ofthe strains experienced by machine 100 during operation.

In accordance with certain embodiments, computer 206 may also executesoftware that performs fatigue life calculation processes to estimatethe life of a given machine component and/or work machine 100. Thefatigue life calculation process may accept strain values that arecalculated from the multiplication of the external loads on thecomponent by the influence coefficient matrix as input, or directlymeasured strain values may serve as input. In this manner, an additionalmeasure of system robustness can be attributed to monitoring system 200.

In certain embodiments, fatigue life calculations may be performed basedon the measured data collected and determined in Steps 405 and 410 ofFIG. 4. Additionally, as noted above, fatigue life calculations may beperformed using the calculated strains determined in Step 435 of FIG. 4,and further described in FIG. 7.

Computer 206 may execute a software process that performs fatigue lifealgorithms that estimates the life of one or more components of workmachine 100. Computer 206 may calculate the fatigue life of componentshaving associated with them one or more strain gauges, such as straingauges configured in the form of a wireless node 228. This measuredstrain data, or the strain data calculated in Steps 435 of FIG. 4, maybe used to estimate the accumulated damage in these areas. In oneembodiment, estimated fatigue damage may be determined using rainflowanalysis followed by an application of Miner's rule. Rainflow analysisis a method to count the cycles in complex, random loading ofcomponents. Miner's rule may then be used to sum the resulting damage ateach point of interest of a component. This information, may provide anassessment of the structure of work machine 100. The fatigue damageestimated by Miner's rule effectively provides an estimate of remainingstructural life.

For example, to determine the fatigue life of a component, a stress-lifecurve may be used for the welded joints, while a stress-life curve withGoodman mean stress correction may be used for other remainingstructures associated with a component. In addition, separatestrain-life curves may be used as desired for certain locations, such asa strain-life curve with Morrow mean stress calculation. As known in theart, computer 206 may execute software that performs the followingequation may to determine fatigue life based on applied stresses.log N=loga+d·logσ−m·logS

-   -   N: number of cycles    -   a: life intercept; constant for each curve    -   d: d=0 for B50 curve, d=−1.28 for B10 curve    -   σ: standard deviation; constant for each curve    -   m: slope of the curve; constant for each curve    -   S: stress range=2*Sa (Data)

A Goodman mean stress correction may be conducted using the equationshown below.

${\frac{S_{a}}{S_{a\; 0}} + \frac{S_{m}}{S_{u}}} = 1$

-   -   S_(a): stress amplitude with S_(m) (Data)    -   S_(a0): modified stress amplitude for 0 mean stress    -   S_(m): mean stress (Data)    -   S_(u): ultimate strength=material constant

The fatigue damage estimated by Miner's rule effectively provides anestimate of remaining structural life. Fatigue life calculationsutilizing a strain-life approach may be carried out using one of theequations below.

$ɛ_{a} = {\frac{\Delta ɛ}{2} = {{\left( \frac{\sigma_{f}^{\prime}}{E} \right) \cdot \left( {2N} \right)^{b}} + {ɛ_{f}^{\prime} \cdot \left( {2N} \right)^{c}}}}$

-   -   ε_(a): strain amplitude (Data)    -   Δε: strain range (Data)    -   N: number of cycles    -   σ_(ƒ): fatigue strength coefficient; material constant    -   E: Young's modulus; material constant    -   b: fatigue strength exponent; material constant (negative value)    -   ε_(ƒ): fatigue ductility coefficient; material constant    -   c: fatigue ductility exponent; material constant (negative        value)

Morrow mean stress (S_(m)) correction may be incorporated as in theequation below.

$ɛ_{a} = {\frac{\Delta ɛ}{2} = {{\left( \frac{\left( {\sigma_{f}^{\prime} - S_{m}} \right)}{E} \right) \cdot \left( {2N} \right)^{b}} + {ɛ_{f}^{\prime} \cdot \left( {2N} \right)^{c}}}}$

The fatigue life may also be calculated from strain obtained by usingthe influence coefficient matrix (A) and the external loads as describedabove. In such embodiments, flags associated with the rows of theinfluence coefficient matrix (A) associated with the location ofinterest of a component may be activated by a user or software processexecuted by computer 206. For example, if an unexpected fatigue problemdevelops at some location that is not equipped with a strain gage, therows of the influence coefficient matrix (A) associated with thatlocation could be activated by altering the value of a flag associatedwith those rows.

The results of the fatigue life calculations may reflect estimatedfatigue life for one or more components of machine 100, as well as anestimate for the fatigue life of the entire structure of machine 100, orportions thereof. Computer 206 may store the fatigue life calculationresults in a memory, such as vehicle database 208, for subsequent accessand analysis.

As the work machine 100 continues to perform operations, the present ageof the machine (designated below as t_(now)) may creep into theprobability density function associated with the distribution of fatiguelife ƒ(t). In one embodiment, computer 206 executes software processesto determine an altered fatigue life distribution for that componentbased on Bayes theorem. For example, the probability that the fatiguelife will be less than some arbitrary time in the future, t*, is givenby the following equation.

${p\left( {{life} < t^{*}} \right)} = \frac{\int_{now}^{t^{*}}{{f(t)}{\mathbb{d}t}}}{\int_{now}^{\infty}{{f(t)}{\mathbb{d}t}}}$

The updated probability density function may then be calculated as inthe following equation.

${f_{up}\left( t^{*} \right)} = \frac{\mathbb{d}{p\left( {{life} < t^{*}} \right)}}{\mathbb{d}t^{*}}$

The new expected life (i.e. the mean value of the updated probabilitydensity function) is given by the following equation.

$t_{up\_ mean} = \frac{\int_{now}^{\infty}{t^{*}{f_{up}\left( t^{*} \right)}{\mathbb{d}t^{*}}}}{\int_{now}^{\infty}{{f_{up}\left( t^{*} \right)}{\mathbb{d}t^{*}}}}$

Applying the Bayesian approach by computer 206, may avoid user confusionthat may result when the machine hours exceed an original estimated timefor crack initiation with no visible crack present at the componentlocation under analysis. The results of the Bayesian calculation processare also stored in a memory device, such as database 208, for subsequentaccess and use by other processes consistent with the disclosedembodiments.

Damage Detection

In certain embodiments, computer 206 may also be configured to executesoftware that performs a damage detection process for work machine 100.The damage detection process may include performing a linear regressionanalysis between the calculated strains determined at step 435 of FIG. 4and the measured strain data obtained at step 410. For example, computer206 may perform software processes that generates a function reflectingthe linear regression analysis of the strains calculated in step 435 ofFIG. 4 and the measured strain data determined in step 410 of FIG. 4.The function may be generated based on a graph bounded on the X-axis bythe calculated strains and on the Y-axis by the measured strains. Byanalyzing the slope and/or correlation coefficient of the of the linearregression analysis within this graph, computer 206 may determinewhether damage exists in the component associated with the strains underanalysis. For example, if the slope of the function generated by theregression analysis is not within a certain threshold of “1,” computer206 may determine damage exists in the component. The damage detectedmay be a fatigue crack, a loose bolted joint or other type of failingjoint, and any other form of structural failure associated with acomponent of work machine 100.

This embodiment may be further explained based on the influencecoefficient matrix used to calculate strains. In certain embodiments,the influence coefficient matrix (A) is populated early in the workmachine's life. Thus, strains calculated during this time frame usingmatrix (A) may be not be different from the actual measured values.Later in work machine 100's life, the strain values measured anddetermined by monitoring system 200 may change values. Thus, whencalculating strains at this stage of work machine 100's life, matrix (A)does not accurately reflect the strain response of work machine 100 atthat time. Therefore, comparing the calculated strains with the measuredstrains using the linear regression analysis may result in a functionhaving a slope different from “1,” reflecting the difference in strainvalues between the calculated and measured strains. This difference mayreflect a crack, bend, or similar damage to an analyzed component.Computer 206 may use rules or other forms of intelligence to determinethe level of damage based on the difference of the resulting function'sslope to the target value of slope “1.” For example, the amount ofdetected damage may be proportional to the difference in the function'sslope from “1.” That is, larger differences between the function's slopefrom the target value may represent more damage in the analyzedcomponent.

The results of the damage detection process may be stored in a memorydevice, such as database 208, for subsequent access and use by processesconsistent with the disclosed embodiments. In certain embodiments,computer 206 may report the damage to the operator of work machine 100via a display device or similar warning indicator. Further, computer 206may generate a damage report and store the information in database 208.An off-board system, such as a laptop, server computer, another workmachine's computer, etc., may access database 208 via a communicationnetwork interconnecting the off-board system and work machine 100, suchas a wireline or wireless network. Alternatively, computer 206 mayreceive a request from an off-board system to send damage reports. Inresponse to the request, computer 206 may retrieve the damage reportstored in database 208 and send the report to the requesting off-boardsystem. In another embodiment, computer 206 may perform the damagedetection process in response to the request from the off-board system.Alternatively, computer 206 may perform software processes thatautomatically direct computer 206 at periodic times to perform thedamage detection process and report the results of the process topredetermine target systems, such as a particular off-board system. Itshould be noted that the damage detection results may be accessed andprocessed by any type of on-board or off-board system, and the aboveexamples are not intended to be limiting to the disclosed embodiments.

Operation Classification

As explained, methods and systems consistent with certain embodimentsmay determine the operation being performed by work machine 100 based ondifferent types of information. For instance, in certain embodiments,computer 206 may execute software that classifies a current operationbeing performed by work machine 100. Classification of the currentoperation may include an analysis of the loads acting on work machine100, as well as any other sensed or derived parameters, including, forexample, inclination relative to the earth, ground speed velocity and/oracceleration, positions, velocities, and accelerations of the implement,and/or pressures. One method of classifying the operation would be touse traditional deterministic software programming techniques.Alternatively, these parameters may be fed into a neural network foranalysis to classify the current operation. In one example, the neuralnetwork may classify the current operation into one of the followingoperations: (1) roading with no load; (2) digging; (3) roading with aload; (4) dumping; (5) idling; (6) bulldozing; (7) back-dragging; and(8) other. Other classifiable operations may be used. Further, while theexemplary operations may be appropriate when work machine 100 is a wheelloader, they may not be appropriate for a different type of workmachine, such as, for example, a motor grader or hauling machine. Thus,computer 206 may execute software that classifies operations that arespecific to the type of work machine 100, which may include the same ordifferent types of classified operations for other types of workmachines.

In embodiments implementing a neural network for operationclassification, database 208 may be installed with a neural network thatis configured to produce output values reflecting certain operationsassociated with the type of work machine 100. FIG. 8 shows a flowchartof an exemplary neural network configuration process consistent withcertain disclosed embodiments. To configure the neural network toperform operation classification functions, actual operational dataassociated with the operation classifications are first recorded in amemory device during operation of work machine 100 (Step 810). Forinstance, sensor data may be collected by computer 206, or anotherdevice configured to collect operational data from work machine 100. Therecorded data may also include time stamp information that reflects whenparticular data values for each sensor data is obtained during machineoperations. The recorded sensor data values (e.g., data valuesreflecting the measured parameter, such as strain, ground speedvelocity, etc.) are designated as inputs, which are assigned to timeperiods associated with the operation of work machine 100 during datacollection. Thus, each data input may include a set of data inputsarranged as a function of time (e.g., input 1(t1), input 2(t2), . . . ,input I (tI), where I may be any positive integer). Based on thisinformation, a user or computer-executed process may assign an operationclassification to each of the time periods associated with the inputdata (Step 820). For example, minimal forces or strains may be appliedto certain components of work machine during idle time periods.Accordingly, the user or software process may assign an idle operationclassification to the time periods having data input values reflectingthese minimal forces or strains.

Once operation classes are assigned to the data inputs for the measuredtime periods, the classified data inputs are fed into a neural networkas inputs in order to train the network to accurately classifyoperations during real time operation of work machine 100 (Step 830).For instance, in one embodiment, the data inputs are applied to theneural network to produce, as output data, a predicted set of classifiedoperations for each time period (e.g., time periods 1-I). Further, workmachine 100 may be exposed to real operations associated with each ofthe classified operations. During these operations, computer 206, oranother internal or external machine device, may collect actual sensordata. Computer 206, or a testing system, may then compare the predictedoutput classification data values with the actual classification of theoperations performed during the real time operations of work machine 100to determine whether the neural network predicts the operations ofmachine 100 during each of the time periods within a predeterminedcriteria. The predetermined criteria may be associated with a thresholdvalue that reflects a maximum acceptable difference between the actualand predicted classification output values. One skilled in the art wouldrecognize that a number of different conditions, thresholds, etc. may beapplied as the predetermined criteria by the disclosed embodiments. Ifthe neural network does not meet the predetermined criteria, the networkmay be adjusted and re-tested until the predetermined criteria is met.Once the neural network produces accurate predicted classifications, thenetwork may be stored in database 208 for subsequent use in classifyingoperations of work machine 100 during later real time operations.

In another embodiment, a process may be implemented that allows a userto classify operations of a work machine under test conditions. In thisexemplary embodiment, a work machine (e.g., work machine 100) mayperform one or more operations over a predetermined period of time.During operation, sensors on the work machine collects measured dataassociated with one or more components of the machine. Further, theoperation of the work machine may be videotaped or monitored in someform. Subsequently, a user may view a time stamped video clip of thework machine during the recorded operations and assign operations tocertain time periods of the operation. This time stamped operation dataand the collected measured data is correlated as classification data asa function of time. The classification data may be fed as the inputsinto the neural network for training the network in a manner similar tothat described above (e.g., train the network until the predeterminedthreshold criteria is met).

In one embodiment of the present invention, when the neural network doesnot meet the predetermined criteria, a user or computer executedprocess, such as program code executed by computer 206, may adjust theweights associated with links corresponding to the nodes within theneural network to compensate for previous inaccurate predictions ofoperation classification output values. For example, if the neuralnetwork includes more than one level of nodes, the weights associatedwith each link interconnecting the layered nodes may be adjusted totrain the network to produce more accurate output values. The weightadjustments may be performed by any number of known algorithms used fortraining neural networks, such as algorithms associated with radialbasis function approximations. One skilled in the art would recognizethat certain embodiments of the present invention may employ differentalgorithms that affect the learning process of the neural network.

Although the above exemplary embodiment describes the neural networkbeing stored in database 208, the network may also be trained after itis stored in a memory device located in machine 100. Further, the neuralnetwork (trained or untrained) may be stored in a memory device internalto computer 206 or any other electronic component within work machine100. As such, embodiments are not limited to the above examples.

Once the neural network is trained and provided in work machine 100,computer 206 may perform an operation classification process thatdetermines the type of operation performed by work machine 100 duringcertain time periods of operation. FIG. 9 is a flowchart of an exemplaryoperation classification process consistent with certain disclosedembodiments. In addition to strain data, computer 206, or anothercomponent of work machine 100, may receive sensor data as inputs fromsensors 210-228 (Step 910). The received sensor data may reflectoperational parameters associated with operations of work machine 100over a period of time. According, the received parameter data may betime stamped by sensor 210-228 or computer 206. The received parameterdata may be checked for reliability, in a manner similar to theprocesses described below in connection with steps 420 of FIG. 4.

The received data may then be fed as inputs into the trained neuralnetwork stored in database 208 (or elsewhere) (Step 920). Additionally,computer 206 may feed other information as inputs to the neural network.For example, unknown load data, free body diagram data, and calculatedstrain data may be used as inputs to the network. The neural networkprocesses the inputs using known neural network processes and producesoutput values. Based on the output values, computer 206 may determinethe classification of an operation performed during certain periods oftime of operation of work machine 100 (Step 930). For instance, based onparameter data values associated with one or more work machinecomponents, computer 206 may determine at time t₁, work machine 100 wasroading with no load, digging, roading with a load, dumping a load, etc.The operation classification information may be stored in a memorylocation within a memory device (e.g., database 208, local memory withincomputer 206, etc.) for subsequent processing consistent with certaindisclosed embodiments.

It should be noted that computer 206 may execute more than one neuralnetwork to perform any of the neural network processes described above.For example, one neural network may be used to classify the currentoperation, while a second neural network may be used to determine theunknown loads of a machine component.

Payload Determination

As described, methods and systems consistent with certain embodimentsenable computer 206 to execute software that estimates the payloadcarried by work machine 100 based on the measured data. Some payloaddetermination systems may require that the operator pause the workmachine and then request an estimate from the system just before dumpingthe load. Pausing the work machine ensured that inertial forces wouldnot corrupt the payload determination. The disclosed embodiments enablepayload determinations to take place without pausing the work machineduring dump operations.

FIG. 10 shows a flowchart of an exemplary payload determination process1000 consistent with certain embodiments. At a step 1004, based on theresults of the operation classification process described above inconnection with FIG. 9, computer 206 determines whether the currentoperation is classified as a certain type of operation, such as a dumpoperation. If the current operation is not classified as a dumpoperation (Step 1004; No), then the payload determination processreturns to step 1002. The payload determination process continues toloop until the current operation is classified as a dump operation. Itshould be noted that the dump operation is an exemplary operation usedby computer 206 during the payload determination process. The disclosedembodiments contemplate using other types of classified operations todetermine whether to calculate the payload of work machine 100.

When the current operation is classified as a dump operation (Step 1004;Yes), computer 206 may perform a kinematics analysis to determine andconsider the motion of work machine 100 and/or one or more of itscomponents, such as work implement 118 (Step 1006). Further, computer206 may perform a kinetics analysis to calculate and determine thepayload mass (Step 1008). The kinetics analysis may include determiningthe mass M from a derived expression for the payload, M=f(q_(i)). In theexpression for the payload, time derivatives of measured quantities qappear. These may be calculated via numerical differentiation using athree-point central difference method, shown in the equation below.

${\frac{\mathbb{d}q}{\mathbb{d}t}\left( t_{n} \right)} \approx \frac{q_{n + 1} - q_{n - 1}}{2\Delta\; t}$

In the equation, Δt is the time increment between each sampled value.Determining the mass of the load may be performed by solving for thenecessary unmeasured variables, such as, for example, loads atnon-instrumented pins and other unknowns. In one exemplary embodiment,computer 206 may execute software that solves, for example, a 10×10system for ten variables that contribute to determining the payload ofwork machine 100. In one exemplary embodiment, the 10×10 system may becombined into a single, derived analytical expression to determine thepayload using methods known in the art. The derived expression for thepayload, M=f(q_(i)) may account for all inertial effects during theloading and dumping process. The expression M=f(q_(i)) is derived from aset of Newtonian equations of motion for the front linkage of workmachine 100. Algebraic manipulations are performed to reduce all of theequations to a form M=f(q_(i)). The Newtonian equations of motioninvolve variables related to inertial effects so that these effects canbe accounted for in the payload calculation. Accordingly, an operatormay no longer need to pause work machine 100 to allow the machine tocalculate its payload. Further, computer 206 may be configured withsoftware that, based on the operation classification of work machine100, automatically determines payload at predetermined times, such aswhen work machine is about to perform a dump operation, during roadingwith load, etc.

In one exemplary embodiment, computer 206 may execute software thatdetermines different stages of a dump operation once this operation isclassified. For instance, computer 206 may execute software thatdetermines different stages of a dump operation based on the positionsand load data of one or more components of machine 100 during aclassified dump operation. Accordingly, computer 206 may detect whenwork machine is beginning, performing, and ending a dump operation.Based on this knowledge, computer 206 may perform the above describedkinematics and kinetic analysis processes at both the beginning and endof the determined dump operation. In this regard, computer 206 maydetermine the mass of the delivered material by determining thedifference between the mass of the payload at the beginning of the dumpoperation and at the end of the dumping operation (Step 1010).Therefore, the payload calculation may reflect the mass of deliveredmaterial, even when only a part of the payload is dumped.

In one exemplary embodiment, the determined payload may be optionallydisplayed in an operator interface located in the operators' station 110(Step 1012). This may allow an operator to track the weight of materialbeing dumped by work machine 100. In addition to the payload amount, thedisplay may convey additional information to the operator, including,for example, an impending tip-over alert and a maximum load scenario,both of which may be determined by computer 206 based on determined loadand strain data, as well other measured parameters, such as inclinationrelative to the Earth. The display could be in the form of an audiblenoise, lights, and a liquid-crystal display, among others.

Computer 206 may store data reflecting the calculated payload in amemory device, such as vehicle database 208. To this end, computer 206may perform a process that determines a cumulative payload for a giventime period based on previously calculated and stored payloadinformation. The cumulative payload information may be maintained indatabase 208, and displayed in a display device in operator's station110, and downloaded off-board work machine 100 for subsequentprocessing.

In another embodiment, computer 206 may execute neural network softwarethat is trained to determine payload of machine 100 based on measuredstress data, determined load data, and other collected parameterinformation. In another embodiment, monitoring system 200 may interfacewith some other pre-existing payload determination system rather thanrely on the processes for payload determination described here.

INDUSTRIAL APPLICABILITY

Methods and systems consistent with the disclosed embodiments usecollected sensor data and calculated strains, loads, and operationalinformation, to provide estimates of fatigue life, payload, and damagestate of one or more components of a work machine. This information isused to provide insight on the fatigue life and health of the workmachine, and to gather information useful for future design improvementsof work machines. In certain embodiments, the information determined byhealth and usage monitoring system 200 may be useful to design futurework machines, operate work machines, to determine resale values basedon known wear of work machine 100, and/or when to perform maintenanceand repair. For example, the health information obtained be thedisclosed embodiments may be used to design components of a work machinethat account for wear that has been analyzed from real time operation ofsimilar machines. In addition, health and usage monitoring system 200may provide health information that is relevant and useful to a numberof entities, including machine operators, work machine purchasers,service mechanics, and work machine developers and engineers. Suchrelevant information may include, 1) cumulative damage data, 2) machineoperation distribution, 3) extreme load cases for each component, 4)load histories at various severity levels, 5) damage rate histogram, and6) crack detection. Each of these items is described further below inturn.

Information regarding cumulative damage data may be stored withinvehicle database 208 and may be made accessible to one or more users orcomputer 206. In certain embodiments, health and usage monitoring system200 may continuously update the stored data that is representative ofthe structural health and usage of the monitored component of workmachine 100. Users or computer processes may access the cumulativefatigue data to estimate the residual life and/or value of a particularcomponent, set of components, or work machine. Such information isrelevant to those purchasing and/or selling work machines that have beenpreviously operated.

In one exemplary embodiment, instead of continuously storing damagerelated data for all locations of each of the monitored components,health and usage monitoring system 200 may track accumulated damage onlyat discrete locations, such as at the locations where one or moresensors are actively sending signals to computer 206. Further, healthand usage monitoring system 200 may optionally accumulate damage datavia the calculated strain at a number of desired component locations, asdetermined by a user. In one exemplary embodiment, the cumulative damagemay be partitioned into portions attributable to each of the variouswork machine operations. For example, in a wheel loader work machine,health monitoring system may store cumulative damage data in matrixform. Each row of the matrix may correspond to a particular location ofa wireless node 228 and a data value associated with an amount ofdamage. The matrix may be configured in any form, such as a designatedset of columns storing accumulated damage data for each of theclassified operations for that work machine. A related column may alsostore the total damage data for a particular wireless node 228. Thedamage data may include directional information corresponding to themost likely-orientation of a fatigue crack.

Information regarding the different classifiable operations may bestored within vehicle database 208 and made available to one or moreusers or computer systems, such as computer 206. The types of operationsthat may be classified by the disclosed embodiments may vary based onthe type of work machine to work machine. For example, a wheel loadermay have associated classifiable operations such as, for example,roading with no load, digging, roading with a load, dumping, idling,bulldozing, back dragging, and “other.” The classification of theseoperations may be performed either by a neural network or viadeterministic software.

Once classified into a specific operation, computer 206 may executesoftware that determines the amount of time spent performing anoperation in real-time. In one example, computer 206 may store thisinformation in vehicle database 208 as data indicative of a total amountof time that work machine 100 operates in a particular operation. Forexample, based on collected sensor data, and determined load and otherparameter information, computer 206 may determine that work machine 100is entering a digging operation at a time t₁. The operation may continueuntil computer 206 determines that an operation other than digging isbeing performed. At that time, computer designates the end of thedigging operation at a time t₂. The time period between t₁ and t₂ may besummed with the time periods of other digging operations to maintain atotal time period that work machine 100 is operating in a diggingoperation. Computer 206 may perform software that forms this informationin a histogram. Alternatively, computer 206 may download thisinformation to an off-board system that forms the histogram. Thehistogram also may include information showing a total operation timefor each of the other classifiable operations. Alternatively,embodiments may form separate histograms for the other classifiedoperations, or selected combinations of operations. Further, theoperation information maintained by health and usage monitoring system200 may be customized or configured based on desired characteristics.For example, the total time spent working in a classifiable operationneed not reflect a total time over the lifetime of work machine 100.Instead, the total time data for the classified operation may reflectthe amount of time working in the classifiable operation since a lastmaintenance job was performed on the machine, the total amount of timeworking in the classifiable operation at a specific worksite, etc.

Computer 206 also may be configured to execute software that determinesin real-time the amount of fatigue of at least one component of workmachine 100 over a period of time due to a specific operation. Again,referring to the digging operation as an example, when determining thefatigue, computer 206 may determine that the work machine is entering adigging operation at time t₁. The operation may continue until computer206 determines that the digging operation has ended at time t₂. Usingany fatigue life calculation processes described above, computer 206 maydetermine the component's fatigue life by calculating the amount offatigue that occurred during the time period between t₁ to t₂. Summingthe determined fatigue with the total lifetime fatigue that occurredwhile performing the operation may provide a total amount of fatigue dueto the classifiable operation. Further, the total amount of fatigue maybe reflective of fatigue during the work machine's entire operationallifetime, since the machine's last maintenance, since initiating work ata specific worksite, etc. This information may be displayed in ahistogram showing the fatigue for each operation separately,collectively, or for sets of selected operations.

In another embodiment, health and usage monitoring system 200 may storeinformation regarding the extreme (e.g., maximum or minimum)instantaneous load scenarios over a machine's or component's lifetime.For example, referring to FIG. 11, a body 1100 is shown under theinfluence of any number of external loads ƒ_(i) (i=1 to n). The body1100 may be a portion of any of the structures comprising work machine100. When any one of the external loads ƒ_(i) complies with apre-established factor, such as surpassing a previously stored lifetimemaximum or minimum load value, then computer 206 may update vehicledatabase 208 with the new lifetime maximum or minimum value. By storingboth the lifetime maximum load and the lifetime minimum load, a totalrange of loading is captured and available for analysis and display on adisplay device in any format.

In one exemplary embodiment, health and usage monitoring system 200 maystore a snapshot of all load values acting on body 1100 at a particularpoint in time. For example, when any one load value exceeds apre-established factor of a lifetime maximum or minimum value, computer206 stores data regarding all the applied load values for body 1100. Inone exemplary embodiment, each of the external loads ƒ_(i) may beconsidered an element in a column vector f(t). To save the dataregarding all the applied loads, the complete column vector f(t) may besaved. It should be noted that each body and each load being monitoredby health and usage monitoring system 200 may have its own “extreme loadcase matrix.” The extreme instantaneous load scenario providesinformation regarding the most devastating instantaneous load applied tothe body 1100 for each applied load.

In addition, health and usage monitoring system 200 may associate thelifetime maximum and minimum load values with the work machine's currentoperation classification in memory. The load data and correspondingoperation classification information may be used in designing anddeveloping components for avoiding yield or buckling failures. Thus, incertain embodiments, computer 206, or another machine system, may sendthe stored load and operation classification information to an off-boardcomputer system for subsequent analysis, such as design, manufacturing,and diagnostic analysis.

In certain embodiments, computer 206 may execute software that stores indatabase 208 information associated with the load histories at variousseverity levels for each classified operation over one or more selectedtime periods (e.g., t₁ to t₂). This information may be provided to theoperator of work machine 100 or to off-board systems for analysis bycomputer-executed software or a user. For example, the storedinformation may include data reflecting the loads applied to a givencomponent determined at time t₁ (e.g., the beginning of a classifiedoperation) to time t₂ (e.g., the end of the classifiable operation) fora desired severity level. A desired severity level may be represented asa percentage value reflecting a level of damage experienced by a givencomponent(s). For example, a 100% severity level may reflect the mostdamaging example experienced by the component or machine during a givenoperation. A 50% severity level may represent the average damageexperienced by the component or machine during the operation.Accordingly, for example, a user or software process may direct computer206 to store the load history of the component at the 90th percentileseverity level for a digging operation. Computer 206 collects and storesall the loads that occur during the digging operation when the loadsamount to a severity level at the 90th percentile. The determination asto whether a particular loading history is at a 90^(th) percentileseverity level may be made by comparing the damage rate for thatparticular operation with a known 90^(th) percentile damage rateobtained from the damage rate histogram (contained in database 208)associated with some component of work machine 100. In one exemplaryembodiment, when a newly collected load history is associated with aseverity level that is closer in value to a pre-selected severity level(e.g., a desired level set by a user) than that of a previously savedload history associated with the same severity level, the newlycollected load history may be replace the previously stored history indatabase 208.

To better illustrate this exemplary embodiment, FIG. 12 shows aflowchart for storing load history information in vehicle database 208.Initially, work machine 100 performs an operation. During the currentoperation, computer 206 collects measured data and load data in a mannerconsistent with the process steps described above in connection withFIG. 4 (Step 1205). Computer 206 collects the measured data associatedwith a given component of work machine 100 at a time t₁. Recording maybe based on a pre-established first triggering event. In one exemplaryembodiment, the triggering event may be associated with the initiationof a classified operation, as determined in a manner consistent with theabove disclosed embodiments. For instance, referring to the abovementioned digging operation as an example, computer 206 may detect afirst triggering event when it determines work machine 100 has begun adigging operation.

At some point, computer 206 stops recording load data at apre-established second triggering event at time t₂. The secondtriggering event may be an indication that a classified operation hasended, as determined by computer 206 in a manner consistent with theabove disclosed embodiments.

Computer 206 may also determine the current operation performed by workmachine 100 during the monitored time period t₁<t<t₂ (Step 1207). In oneembodiment, computer may execute a neural network to classify thecurrent operation in a manner similar to that described above inconnection with FIG. 9. Additionally, computer 206 may collect load datadetermined by computer 206 in a manner consistent with the disclosedembodiments. The load data may be used to populate a candidate loadmatrix (Step 1208). The candidate load matrix reflects a data structureincluding the load history for the component or work machine 100 duringthe current operation for the given time period t. Thus, the stored loaddata in the matrix may include all the loads applied to the analyzedcomponent over the time period t₁<t<t₂.

Computer 206 may also determine a target damage rate for one or morecomponents of work machine 100 for the time period t. The target damagerate corresponds to the desired severity level percentile. Accordingly,if the desired severity level is the 90th percentile for the exemplarydigging operation, computer 206 may select the target damage rate tocorrespond to the 90th percentile damage rate. In certain embodiments,computer 206 may access and analyze a damage rate histogram to determinethe target damage rate. For example, the body 1100 of FIG. 11 is shownunder the influence of any number of external loads ƒ_(i) (i=1 to n).The load history for the i^(th) operation, f_(i)(t) may give rise to acorresponding damage field, D_(i)(r,t), which is increasing with time.Accordingly, for each of the different operations performable by workmachine 100, a complete finite time history (of length t₂−t₁) of f(t)may be selected by monitoring the residual of the following equation.

$\frac{{\int_{S}{{w(r)}{D_{i}\left( {r,t_{2}} \right)}{\mathbb{d}A}}} - {\int_{S}{{w(r)}{D_{i}\left( {r,t_{1}} \right)}{\mathbb{d}A}}}}{t_{2} - t_{1}} = {{target}\mspace{14mu}{damage}\mspace{14mu}{rate}}$

As explained above, the target damage rate is the damage ratecorresponding to the desired percentile of severity. The damage field,D_(i)(r,t), is the amount of damage that exists on the component at timet at a particular surface location r (a position vector in the body'slocal coordinate system) attributable to the i^(th) operation. Aweighting function, w(r), is a measure of the relative importance offailure at various locations on the surface of the body being monitored.For example, a weld failure at one location on a body might be moredamaging, and more expensive to repair, than a similar failure onanother part of the body. The time interval, t₂−t₁, for the load historyf_(i)(t) may be equal to the duration of one instance of that operationthat most closely satisfies the target damage rate equation. In thetarget damage rate equation above, the quantities are shown in integralform in order to conveniently convey the concept. In another exemplaryembodiment, however, the surface integrals may be approximated in theform of a weighted summation. A total damage field, D_(total)(r,t), maybe determined using the following equation.

${D_{total}\left( {r,t} \right)} = {\sum\limits_{i = 1}^{\#\mspace{14mu}{of}\mspace{14mu}{ops}}{D_{i}\left( {r,t} \right)}}$

It should be noted that the direction of damage used for the summationin the above equation must be the same for all of the classifiableoperations. Also, it should be noted that an amount of total damage maybe different on every plane. Accordingly, the plane having the highestamount of total damage must be the plane utilized when solving for thetarget damage rate. Again, the location of each wireless node 228 maypotentially have a different plane of maximum damage. The target damagerate may be determined prior to or during the current operation of workmachine 100.

Computer 206 may also calculate the current damage rate for thecomponent over a time period t of the single digging operation, wheret₁<t<t₂ (Step 1210). Computer 206 may determine the total damage usingthe equations for determining the target damage rate described above,based on the measured data associated with the current operation of thework machine.

Once the target damage amount is obtained, and the current damage rateis determined, computer 206 may evaluate a residual of the currentdamage rate (Step 1215). A residual represents a degree to which thetarget damage rate (determined above) is not satisfied. In oneembodiment, computer 206 may determine the current residual of thecurrent damage rate by calculating a damage factor residual based uponthe target damage rate. The target damage factor residual reflects adifference between the desired target damage rate (in this example, the90^(th) percentile) and the actual damage rate determined by computer206. It may be expected that some small amount of residual may exist, asany externally applied load history may not exactly give rise to thetarget damage rate (in this example, the 90^(th) percentile).

Once the current residual determined, computer 206 may collect apreviously stored residual from a memory device within work machine 100,such as database 208 (Step 1220). The previous residual corresponds to aresidual that was previously determined by computer 206 based on aprevious operation similar to the current operation of work machine 100,determined in Step 1207. Also, computer 206 may previously havegenerated and stored in database 208 a previous load matrix associatedwith the previous residual.

At step 1225, computer 206 may compare the current residual with thepreviously stored damage residual. Based upon the comparisons computer206 determines whether the new load history (i.e., candidate loadmatrix) more closely corresponds to the desired target damage rate bydetermining whether the current residual is less than the previousresidual. If the current residual is not less than the previous residual(Step 1225; No), then computer 206 determines the previously storedresidual, with its associated load history, is closer to the targetseverity level (such as the 90th percentile) than the newly calculatedresidual with its load history (i.e., the candidate load matrix).Accordingly, computer 206 flushes the current residual and candidatematrix (1245), and the may return to step 1205 to monitor the nextoperation.

If, however the current residual is less than the previous residual(Step 1225; Yes), computer 206 may determine the newly calculatedresidual with its associated new load history (i.e., candidate loadmatrix), is closer to the target severity level (such as the 90thpercentile) than the previously stored residual with its associated loadhistory (i.e., previously stored load matrix). Accordingly, computer 206replaces the previously stored residual with the current residual, andthe load history associated with the previous residual (i.e., previousload matrix) is replaced with the current load history (i.e., candidateload matrix) in database 208 (Step 1250). In this manner, health andusage monitoring system 200 may continuously monitor for a load historythat most closely matches the desired load severity. The process mayreturn to Step 1205 to monitor for the next operation.

Although in the example described, the desired severity level was the90^(th) percentile, it is contemplated that the disclosed embodimentsmay store the load history for a classified operation at any desiredpercentile. In one example, computer 206 is configured to executesoftware that stores the load histories for each operation at thelifetime maximum (i.e., 100th percentile). Alternatively, computer 206may store load histories for sets of severity levels, such as the 90th,50th, and 10th percentile for each component and each operation. Storingthe load history between time t₁ and t₂ for each operation at a desiredseverity level provides a manageable amount of data for analysis, thusreducing the amount of memory space used to store load history data.Accordingly, a user or software executed process may access database 208to view and/or analyze the load histories for each desired severitylevel and thus obtain operation profiles of work machine 100.

Additionally, as mentioned above, information for generating a damagerate histogram may be stored in vehicle database 208 and made accessibleto a user or computer system, such as computer 206 or an off-boardcomputer system. For example, FIG. 13 shows a damage rate histogram 1300for an exemplary operation of work machine 100. In this exemplaryembodiment, histogram 900 may be developed to represent the damage rateover the lifetime of work machine 100 for the single classifiedoperation, such as the digging operation.

As shown in FIG. 13, the x-axis of histogram 1300 may represent damagerates with increasing severity. The y-axis may represent percentage ofhours spent at each damage rate while performing the respectiveclassified operation. Computer 206, or other system components of workmachine 100, may collect the information for generating the histogram.For example, sensors 210-228 may measure and collect certain types ofdata, such as strain data values. Computer 206 may use the collecteddata to perform the fatigue life calculations described above consistentwith certain disclosed embodiments. Therefore, computer 206 maydetermine the length of time for a single classified operation timeinterval (e.g., t₁<t<t₂). The change in determined damage may be dividedby the time period, and the quotient will determine the damage rates,which computer 206 (or another computer-based system) formats into datathat is provided to a display device for display. Computer 206 may usethe result of the calculated weighted integral damage rate, determinedusing the target damage equation described above, to update thehistogram 1300. After the histogram has been populated, a damage rate ofany severity level (e.g., 10th, 50th, 90th, or 100th percentile) for anyoperation may be obtained for use in selecting the load history ofcorresponding severity.

In another embodiment, computer 206 may store information in database208 reflecting the detection of a crack or loosely fitted partassociated with a component of work machine 100. As explained above,methods and systems of the disclosed embodiments may detect a crackbased on a comparison/cross-plot of calculated and measured strain forthe component. For instance, computer 206 may consider the slope andcorrelation coefficient derived from a cross-plot and least-squareslinear fit of calculated vs. measured strain for any given strainchannel of the component. Computer 206 may use this information as anindication of crack initiation in the given component and provide awarning to a user or other computer system. Thus, a user may be warnedof pending cracks in a machine component. For example, computer 206 mayprovide a warning of crack initiation for a given component to theoperator or owner of work machine 100, via a display device or warningpanel. Alternatively, or additionally, computer 206 may provide thewarning to a software process executing in another computer system, suchas an ECM controlling engine operations. In response to the warning, thecomputer system may perform a process to avoid further damage to thecomponent, such as reducing engine idle speed, stopping the engine, etc.

It should be noted that the information contained in vehicle database208 may be used to extrapolate accumulated damage associated with one ormore components of work machine 100, or of work machine 100 itself, evenif one or more of sensors 210-228 have ceased to function. For example,consider a situation where all of the instrumented pins and wirelessstrain gages implemented on machine 100 have ceased to function, andonly vehicle speed, cylinder displacement, and cylinder force sensorsremain operational. Computer 206 may still be able to determine theclassified operation of work machine 100 based on the availableparameters obtained from the operational sensors. For instance, duringan particular operation, computer 206 may match the average powerexpended by the cylinders to the appropriate “damage-rate bin” in thedamage rate histogram 1300 for that operation, based upon previouscorrelations made by computer 206. In this manner, computer 206 mayestimate an additional amount of damage without any fatigue lifecalculations based upon strain, either calculated or measured.

As explained, methods and systems consistent with the disclosedembodiments enable a user or off-board system to collect health andusage information from work machine 100. FIG. 14 shows a flow chart 1450of an exemplary data analysis process consistent with certainembodiments. In one example, computer 206, or another computer system,may transfer information from vehicle database 208 to an off-boardsystem, such as an external database (Step 1452). In one embodiment, theoff-board system may be configured to receive and analyze informationfrom a plurality of work machines.

At step 1454, the off-board system may perform statistical analysis ofthe information. For example, when information from multiple workmachines has been downloaded to an off-board database, the off-boardsystem, a user, or another computer system, may access the database tocompare and analyze the machine information for structural integrity ofone or more components of work machine 100, and the other machines. Theinformation may be analyzed for, among other things, abusive use of theanalyzed machine. Further, the off-board system may rank the remainingfatigue life of one or more work machines (Step 1456).

Steps 1458 to 1468 show a number of possible uses of the informationdownloaded from work machine 100. For example, at step 1458, a servicecontract may be priced based on the fatigue life evaluated at step 1456.The service contract may be priced to take into account the remainingfatigue life, as well as any rough handling due to heavy loading thatmay have been applied to work machine 100.

At step 1460, the data may be screened for condition-based maintenancethat should be performed. This may include evaluating the health relateddata for work machine 100 to determine which components are most in needof maintenance based upon their remaining life or their condition.Accordingly, components having a short remaining fatigue life may bemaintained or replaced to ensure efficient and continuous operation ofwork machine 100.

At a step 1462, sales forecasts may be created by dealers based upon theremaining fatigue life of the work machines. Accordingly, dealers may beable to predict the future needs of a customer and thereby create salesforecasts. At a step 1464, engineers may design or generate new designsfor work machine 100 based upon the evaluated fatigue life and theinformation obtained from work machine 100. For example, using theinformation obtained from work machine 100, engineers may be able toremove excess material from components or areas of components that maynot receive high stress. In one exemplary embodiment, new designs may bebased upon the downloaded health information.

In step 1466, efficiencies of a work site including one or more workmachines that include health and usage monitoring system 200 may beevaluated. In one exemplary embodiment, an off-board system may reviewand analyze the amount of time a work machine, or a set of work machine,performs a specific operation. For example, if an inordinate amount oftime is spent roading without a load, then that efficiency may be notedand corrected to create a more efficient work site.

In step 1468, the efficiency of specific operators may be evaluatedusing the information obtained from vehicle database 208. For example,the information may indicate that one operator is more efficient thananother operator in performing certain operations of a type of workmachine at a work site. Using such information, work site managers maybe able to recommend additional training or additional operators tomaintain an efficient work site. Other uses for the information obtainedfrom health and usage monitoring system 200 may also be available.

Although health and usage monitoring system 200 is described withreference to a work machine, it should be noted that methods and systemsconsistent with the disclosed embodiments may be used with structuresand components other than work machines. For example, health and usagemonitoring system 200 may be used to monitor any structural systemsubject to fatigue life. In some embodiments, health and usagemonitoring system 200 may be used to monitor any structural system thatmay be used to perform a plurality of operations. Some examples of otherstructures that may incorporate health and usage monitoring system 200disclosed herein may include civil structures, aircraft, andautomobiles. Other structures may equally benefit from the systemdescribed herein.

Methods and systems consistent with the disclosed embodiments provideuseful information that allows operators, user, and computer systems toassess the health of a work machine and perform further analysis basedon this information. For instance, because health and usage monitoringsystem 200 gathers data related to structural mechanics and estimatesstructural life in near real-time, it may be used to monitor thestructural integrity of a structure, such as, for example a workmachine, throughout the structure's lifespan. Moreover, as mentionedabove, health and usage monitoring system 200 also may be useful forproviding information to assist the design, manufacture, operation,resale, and repair of components and work machines. For example, overtime, the information obtained by health and usage monitoring system 200may be used to more efficiently design and more accurately analyzecomponents of a work machine. The structures may then be redesigned andmanufactured with, for example, different materials and dimensions thatmay be lighter, less expensive, stronger, etc., while still providingacceptable performance in the field.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the disclosed embodimentswithout departing from the scope of the invention. Other embodiments ofthe disclosed embodiments will be apparent to those skilled in the artfrom consideration of the specification and practice of the disclosedembodiment. For example, the process steps shown in the disclosedfigures may be performed in different order, and are not limited to thesequences illustrated therein. Also, additional or fewer process stepsmay be implemented during these processes. Further, although thedisclosed embodiments describe computer 206 executing softwareassociated with neural networks to perform specific processes, methodsand systems consistent with the disclosed embodiments may allow computer206 to request another system to execute this software and report itsresults to computer 206. Further, computer 206 may download neuralnetwork software from off-board systems prior to, or subsequent to, thenetwork being trained. In another embodiment, the payload determinationprocess may be performed for operations other than dump operations. Forinstance, computer 206 may perform payload determination processes whenwork machine 100 is roading with a load, etc.

Further, an off-board system, as the term is used herein, may representa system that is located remote from work machine 100. An off-boardsystem may be a system that connects to the work machines throughwireline or wireless data links. Further, an off-board system may be acomputer system including known computing components, such as one ormore processors, software, display, and interface devices that operatecollectively to perform one or more processes. Alternatively, oradditionally, an off-board system may include one or more communicationdevices that facilitate the transmission of data to and from the workmachines. In certain embodiments, an off-board system may be anotherwork machine remotely located from work machine 100.

Additionally, although the disclosed embodiments are described as beingassociated with data and software programs stored in memory and otherstorage mediums, one skilled in the art will appreciate that theseembodiments may also be stored on or read from other types ofcomputer-readable media, such as secondary storage devices, like harddisks, floppy disks, optical storage devices, DVDs, or CD-ROM; a carrierwave from a communication link or network, such as the Internet; orother forms of RAM or ROM. It is intended that the disclosed embodimentsand described examples be considered as exemplary only, with a truescope of the invention being indicated by the following claims and theirequivalents.

1. A method of analyzing the use of a work machine comprising: providinga computer with a neural network on the machine; inputting data to thecomputer, at least a portion of the data associated with a loadexperienced by one of the components of the machine; and using theneural network to classify a current physical operation of the machineas one of a plurality of types of operations performable by the machine,based on the inputted data associated with the load experienced by oneof the components of the machine.
 2. The method of claim 1, furtherincluding: generating a histogram of the different types of physicaloperations of the machine.
 3. The method of claim 1, including:determining fatigue of the one component over a first time period ofoperation of the machine for at least one of the classified types ofoperations.
 4. The method of claim 1, wherein using the neural networkto classify the current physical operation is performed in real-time bythe computer during operation of the machine.
 5. The method of claim 1,wherein using the neural network to classifying the physical operationis performed by logic-based software executed by the computer.
 6. Themethod of claim 1, wherein using the neural network to classify thephysical operation includes: comparing the inputted data to stored datacorresponding to physical operations performable by the machine; andidentifying one of the different types of operations as the currentphysical operation of the machine based on the comparison.
 7. The methodof claim 1, wherein inputting the data includes: collecting strain dataassociated with the component from one or more strain gauges.
 8. Themethod of claim 7, including: communicating the collected strain data tothe computer over a wireless network.
 9. A system for classifying aphysical operation performed by a machine, comprising: sensors disposedabout the machine, each of the sensors being configured to detect one ormore parameters associated with the machine; a first memory storingclassification data associated with different types of physicaloperations performable by the machine; and a processor including aneural network; wherein the processor is configured to receive a signalindicative of at least one of the detected parameters and use the neuralnetwork to classify a current physical operation of the machine as atleast one of the different types of operations performable by themachine, based upon the received signal and the stored classificationdata.
 10. The system of claim 9, including: a second memory, included onthe machine, that stores data reflecting the classified operation. 11.The system of claim 9, wherein the processor is configured to identify alevel of fatigue associated with the machine during the classifiedoperation over a selected time period.
 12. The system of claim 9,further including an off-board system configured to receive dataassociated with the classified operation.
 13. The system of claim 9,wherein the processor is configured to classify the physical operationin real-time.
 14. The system of claim 9, wherein the neural networkincludes stored parameters for each of the plurality of different typesof operations.
 15. The system of claim 14, wherein the processor isconfigured to: compare the one or more detected parameters to storedparameters and determine which physical operation includes data thatmost closely matches the detected parameters; and classify the physicaloperation based on the determination.
 16. A method of analyzing the useof a machine comprising: providing a computer with a neural network onthe machine; inputting data to the computer, at least a portion of thedata associated with a load experienced by one of the components of themachine; using the neural network to classify a current physicaloperation of the machine as one of a plurality of types of operationsperformable by the machine, based on the inputted data associated withthe load experienced by one of the components of the machine; andgenerating a histogram of the different types of physical operationsperformed by the machine.
 17. The method of claim 16, wherein using theneural network to classify the current physical operation is performedin real-time by the computer during operation of the machine.
 18. Themethod of claim 16, wherein inputting the data includes: collectingstrain data associated with the component from one or more straingauges; and communicating the collected strain data to the computer overa wireless network.
 19. The method of claim 17, wherein using the neuralnetwork to classify the physical operation includes: comparing theinputted data to stored data corresponding to physical operationsperformable by the machine; and identifying one of the different typesof operations as the current physical operation of the machine based onthe comparison.